Deep convolutional networks are often used today in applied problems as one of the basic components of learning systems. On some tasks, for example, the task of modeling faces, it is possible to achieve representations with good interpretability[1]}. However, this success does not translate to all types of data equally. Problems begin in those tasks where generalization is required for a small amount of data; where datasets are poorly balanced; where active learning is required and catastrophic forgetting must be avoided[2]}[3]}; A separate type of difficulty is the selection of architecture hyperparameters[4]}.
Finally, it is known that the representation of deep convolutional networks is not object-oriented(details below), while in human representation proto-objects appear already at the early stages of processing of visual information[5]}.
This paper will examine how the use of the principles of saccadic vision might be helpful in fixing some of the above problems.
